The present invention relates generally to communications systems and, in particular, to speech quality assessment.

Performance of a wireless communication system can be measured, among other things, in terms of speech quality. In the current art, there are two techniques of speech quality assessment. The first technique is a subjective technique (hereinafter referred to as "subjective speech quality assessment"). In subjective speech quality assessment, human listeners are used to rate the speech quality of processed speech, wherein processed speech is a transmitted speech signal which has been processed at the receiver. This technique is subjective because it is based on the perception of the individual human, and human assessment of speech quality typically takes into account phonetic contents, speaking styles or individual speaker differences. Subjective speech quality assessment can be expensive and time consuming.

The second technique is an objective technique (hereinafter referred to as "objective speech quality assessment"). Objective speech quality assessment is not based on the perception of the individual human. Most objective speech quality assessment techniques are based on known source speech or reconstructed source speech estimated from processed speech. However, these objective techniques do not account for phonetic contents, speaking styles or individual speaker differences.

Accordingly, there exists a need for assessing speech quality objectively which takes into account phonetic contents, speaking styles or individual speaker differences.

EP 1187 100 A1 discloses a method for objective speech quality assessment without reference signal.

US 4,352,182 discloses a method for testing the quality of digital speech-transmission equipment.

The present invention is a method for objective speech quality assessment that accounts for phonetic contents, speaking styles or individual speaker differences by distorting speech signals under speech quality assessment. By using a distorted version of a speech signal, it is possible to compensate for different phonetic contents, different individual speakers and different speaking styles when assessing speech quality. The amount of degradation in the objective speech quality assessment by distorting the speech signal is maintained similarly for different speech signals, especially when the amount of distortion of the distorted version of speech signal is severe. Objective speech quality assessment for the distorted speech signal and the original undistorted speech signal are compared to obtain a speech quality assessment compensated for utterance dependent articulation. In one embodiment, the comparison corresponds to a difference between the objective speech quality assessments for the distorted and undistorted speech signals.

The features, aspects, and advantages of the present invention will become better understood with regard to the following description, appended claims, and accompanying drawings where:

• Fig. 1 depicts an objective speech quality assessment arrangement which compensates for utterance dependent articulation in accordance with the present invention;
• Fig. 2 depicts an embodiment of an objective speech quality assessment module employing an auditory-articulatory analysis module in accordance with the present invention.;
• Fig. 3 depicts a flowchart for processing, in an articulatory analysis module, the plurality of envelopes a_i(t) in accordance with one embodiment of the invention; and
• Fig. 4 depicts an example illustrating a modulation spectrum A_i(m,f) in terms of power versus frequency.

The present invention is a method for objective speech quality assessment that accounts for phonetic contents, speaking styles or individual speaker differences by distorting processed speech. Objective speech quality assessment tend to yield different values for different speech signals which have same subjective speech quality scores. The reason these values differ is because of different distributions of spectral contents in the modulation spectral domain. By using a distorted version of a processed speech signal, it is possible to compensate for different phonetic contents, different individual speakers and different speaking styles. The amount of degradation in the objective speech quality assessment by distorting the speech signal is maintained similarly for different speech signals, especially when the distortion is severe. Objective speech quality assessment for the distorted speech signal and the original undistorted speech signal are compared to obtain a speech quality assessment compensated for utterance dependent articulation.

Fig. 1 depicts an objective speech quality assessment arrangement 10 which compensates for utterance dependent articulation in accordance with the present invention. Objective speech quality assessment arrangement 10 comprises a plurality of objective speech quality assessment modules 12, 14, a distortion module 16 and a compensation utterance-specific bias module 18. Speech signal s(t) is provided as inputs to distortion module 16 and objective speech quality assessment module 12. In distortion module 16, speech signal s(t) is distorted to produce a modulated noise reference unit (MNRU) speech signal s'(t). In other words, distortion module 16 produces a noisy version of input signal s(t). MNRU speech signal s'(t) is then provided as input to objective speech quality assessment module 14.

In objective speech quality assessment modules 12, 14, speech signal s(t) and MNRU speech signal s'(t) are processed to obtain objective speech quality assessments SQ(s(t) and SQ(s'(t)). Objective speech quality assessment modules 12, 14 are essentially identical in terms of the type of processing performed to any input speech signals. That is, if both objective speech quality assessment modules 12, 14 receive the same input speech signal, the output signals of both modules 12, 14 would be approximately identical. Note that, in other embodiments, objective speech quality assessment modules 12, 14 may process speech signals s(t) and s'(t) in a manner different from each other. Objective speech quality assessment modules are well-known in the art. An example of such a module will be described later herein.

Objective speech quality assessments SQ(s(t) and SQ(s'(t)) are then compared to obtain speech quality assessment SQ_compensated, which compensates for utterance dependent articulation. In one embodiment, speech quality assessment SQ_compensated is determined using the difference between objective speech quality assessments SQ(s(t) and SQ(s'(t)). For example, SQ_compensated is equal to SQ(s(t) minus SQ(s'(t)), or vice-versa. In another embodiment, speech quality assessment SQ_compensated is determined based on a ratio between objective speech quality assessments SQ(s(t) and SQ(s'(t)). For example, $${\mathrm{SQ}}_{\mathrm{compensated}}=\frac{\mathrm{SQ}\left(\mathrm{s}\left(\mathrm{t}\right)\right)+\mathrm{\mu }}{\mathrm{SQ}\left(\mathrm{sʹ}\left(\mathrm{t}\right)\right)+\mathrm{\mu }}\mathrm{or}{\mathrm{SQ}}_{\mathrm{compensated}}=\frac{\mathrm{SQ}\left(\mathrm{sʹ}\left(\mathrm{t}\right)\right)+\mathrm{\mu }}{\mathrm{SQ}\left(\mathrm{s}\left(\mathrm{t}\right)\right)+\mathrm{\mu }}$$ where µ is a small constant value.

As mentioned earlier, objective speech quality assessment modules 12, 14 are well known in the art. Fig. 2 depicts an embodiment 20 of an objective speech quality assessment module 12, 14 employing an auditory-articulatory analysis module in accordance with the present invention. As shown in Fig. 2, objective quality assessment module 20 comprises of cochlear filterbank 22, envelope analysis module 24 and articulatory analysis module 26. In objective quality assessment module 20, speech signal s(t) is provided as input to cochlear filterbank 22. Cochlear filterbank 22 comprises a plurality of cochlear filters h_i(t) for processing speech signal s(t) in accordance with a first stage of a peripheral auditory system, where i=1,2,...,N_c represents a particular cochlear filter channel and N_c denotes the total number of cochlear filter channels. Specifically, cochlear filterbank 22 filters speech signal s(t) to produce a plurality of critical band signals s_i(t), wherein critical band signal s_i(t) is equal to s(t)*h_i(t).

The plurality of critical band signals s_i(t) is provided as input to envelope analysis module 24. In envelope analysis module 24, the plurality of critical band signals s_i(t) is processed to obtain a plurality of envelopes a_i(t), wherein ${\mathrm{a}}_{\mathrm{i}}\left(\mathrm{t}\right)=\sqrt{{\mathrm{s}}_{\mathrm{i}}^{\mathrm{2}}\left(\mathrm{t}\right)+{\hat{\mathrm{s}}}_{\mathrm{i}}^{\mathrm{2}}\left(\mathrm{t}\right)}$ and ŝ_i(t) is the Hilbert transform of s_i(t).

The plurality of envelopes a_i(t) is then provided as input to articulatory analysis module 26. In articulatory analysis module 26, the plurality of envelopes a_i(t) is processed to obtain a speech quality assessment for speech signal s(t). Specifically, articulatory analysis module 26 does a comparison of the power associated with signals generated from the human articulatory system (hereinafter referred to as "articulation power P_A(m,i)") with the power associated with signals not generated from the human articulatory system (hereinafter referred to as "non-articulation power P_NA(m,i)"). Such comparison is then used to make a speech quality assessment.

Fig. 3 depicts a flowchart 300 for processing, in articulatory analysis module 26, the plurality of envelopes a_i(t) in accordance with one embodiment of the invention. In step 310, Fourier transform is performed on frame m of each of the plurality of envelopes a_i(t) to produce modulation spectrums A_i(m,f), where f is frequency.

Fig. 4 depicts an example 40 illustrating modulation spectrum A_i(m,f) in terms of power versus frequency. In example 40, articulation power P_A(m,i) is the power associated with frequencies 2∼12.5 Hz, and non-articulation power P_NA(m,i) is the power associated with frequencies greater than 12.5 Hz. Power P_No(m,i) associated with frequencies less than 2 Hz is the DC-component of frame m of critical band signal a_i(t). In this example, articulation power P_A(m,i) is chosen as the power associated with frequencies 2∼12.5 Hz based on the fact that the speed of human articulation is 2∼12.5 Hz, and the frequency ranges associated with articulation power P_A(m,i) and non-articulation power P_NA(m,i) (hereinafter referred to respectively as "articulation frequency range" and "non-articulation frequency range") are adjacent, non-overlapping frequency ranges. It should be understood that, for purposes of this application, the term "articulation power P_A(m,i)" should not be limited to the frequency range of human articulation or the aforementioned frequency range 2∼12.5 Hz. Likewise, the term "non-articulation power P_NA(m,i)" should not be limited to frequency ranges greater than the frequency range associated with articulation power P_A(m,i). The non-articulation frequency range may or may not overlap with or be adjacent to the articulation frequency range. The non-articulation frequency range may also include frequencies less than the lowest frequency in the articulation frequency range, such as those associated with the DC-component of frame m of critical band signal a_i(t).

In step 320, for each modulation spectrum A_i(m,f), articulatory analysis module 26 performs a comparison between articulation power P_A(m,i) and non-articulation power P_NA(m,i). In this embodiment of articulatory analysis module 26, the comparison between articulation power P_A(m,i) and non-articulation power P_NA(m,i) is an articulation-to-non-articulation ratio ANR(m,i). The ANR is defined by the following equation $$\mathrm{ANR}\left(\mathrm{m},\mathrm{i}\right)=\frac{{\mathrm{P}}_{\mathrm{A}}\left(\mathrm{m},\mathrm{i}\right)+\mathrm{\epsilon }}{{\mathrm{P}}_{\mathrm{NA}}\left(\mathrm{m},\mathrm{i}\right)+\mathrm{\epsilon }}$$ where ε is some small constant value. Other comparisons between articulation power P_A(m,i) and non-articulation power P_NA(m,i) are possible. For example, the comparison may be the reciprocal of equation (1), or the comparison may be a difference between articulation power P_A(m,i) and non-articulation power P_NA(m,i). For ease of discussion, the embodiment of articulatory analysis module 26 depicted by flowchart 300 will be discussed with respect to the comparison using ANR(m,i) of equation (1). This should not, however, be construed to limit the present invention in any manner.

In step 330, ANR(m,i) is used to determine local speech quality LSQ(m) for frame m. Local speech quality LSQ(m) is determined using an aggregate of the articulation-to-non-articulation ratio ANR(m,i) across all channels i and a weighing factor R(m,i) based on the DC-component power P_No(m,i). Specifically, local speech quality LSQ(m) is determined using the following equation $$\mathrm{LSQ}\left(\mathrm{m}\right)=\log \left[{\displaystyle \sum _{\mathrm{i}=\mathrm{1}}^{{\mathrm{N}}_{\mathrm{c}}}}\mathrm{ANR}\left(\mathrm{m},\mathrm{i}\right)\mathrm{R}\left(\mathrm{m},\mathrm{i}\right)\right]$$ where $$\mathrm{R}\left(\mathrm{m},\mathrm{i}\right)=\frac{\log (\mathrm{1}+{\mathrm{P}}_{\mathrm{No}}\left(\mathrm{m},\mathrm{i}\right)}{{\displaystyle \sum _{\mathrm{k}=\mathrm{1}}^{\mathrm{Nc}}}\log (\mathrm{1}+{\mathrm{P}}_{\mathrm{No}}\left(\mathrm{m},\mathrm{k}\right)}$$ and k is a frequency index.

In step 340, overall speech quality SQ for speech signal s(t) is determined using local speech quality LSQ(m) and a log power P_s(m) for frame m. Specifically, speech quality SQ is determined using the following equation $$\mathrm{SQ}=L{\left\{{\mathrm{P}}_{\mathrm{s}}\left(\mathrm{m}\right)\mathrm{LSQ}\left(\mathrm{m}\right)\right\}}_{\mathrm{m}=\mathrm{1}}^{\mathrm{T}}{\left[{\displaystyle \sum _{\begin{array}{l}\mathrm{m}=\mathrm{1}\\ {\mathrm{P}}_{\mathrm{s}}>{\mathrm{P}}_{\mathrm{th}}\end{array}}^{\mathrm{T}}}{\mathrm{P}}_{\mathrm{s}}^{\mathrm{\lambda }}\left(\mathrm{m}\right){\mathrm{LSQ}}^{\mathrm{\lambda }}\left(\mathrm{m}\right)\right]}^{{}^{\mathrm{1}}{/}_{\mathrm{\lambda }}}$$ where ${\mathrm{P}}_{\mathrm{s}}\left(\mathrm{m}\right)=\log \left[{\displaystyle \sum _{\mathrm{t}\hat{\mathrm{I}}\mathrm{m}}}{\mathrm{s}}^{\mathrm{2}}\left(\mathrm{t}\right)\right],$ L is L_p-norm, T is the total number of frames in speech signal s(t), λ is any value, and P_th is a threshold for distinguishing between audible signals and silence. In one embodiment, λ is preferably an odd integer value.

The output of articulatory analysis module 26 is an assessment of speech quality SQ over all frames m. That is, speech quality SQ is a speech quality assessment for speech signal s(t).

Although the present invention has been described in considerable detail with reference to certain embodiments, other versions are possible. Therefore, the scope of the present invention should not be limited to the description of the embodiments contained herein.